In the task of aspect sentiment quad prediction (ASQP), generative methods
for predicting sentiment quads have shown promising results. However, they
still suffer from imprecise predictions and limited interpretability, caused by
data scarcity and inadequate modeling of the quadruplet composition process. In
this paper, we propose Self-Consistent Reasoning-based Aspect-sentiment
quadruple Prediction (SCRAP), optimizing its model to generate reasonings and
the corresponding sentiment quadruplets in sequence. SCRAP adopts the
Extract-Then-Assign reasoning strategy, which closely mimics human cognition.
In the end, SCRAP significantly improves the model's ability to handle complex
reasoning tasks and correctly predict quadruplets through consistency voting,
resulting in enhanced interpretability and accuracy in ASQP.

通过模型的自洽性推理和相应的情感四元组生成，SCRAP 显著提高了应对复杂推理任务的能力，并通过一致性投票正确预测四元组，从而在 ASQP 中获得了增强的可解释性和准确性。